This project aims to analyze movie ratings and genre preferences to uncover meaningful correlations between various movie attributes such as genre, runtime, and audience ratings. The analysis was conducted using Python within a Jupyter Notebook environment, leveraging various data science libraries for cleaning, analyzing, and visualizing the data.
The primary objective of this project is to explore and identify relationships between different movie characteristics to provide insights that can inform decision-making in the film industry.
The dataset used in this project includes information on over 10,000 movies, including attributes such as genre, runtime, ratings, and more. The data was pre-processed and cleaned to ensure accuracy and consistency in the analysis.
Programming Language: Python Libraries: Pandas, NumPy, Seaborn, Matplotlib Environment: Jupyter Notebook Techniques: Data Cleaning, Statistical Analysis, Data Visualization
Data Cleaning: Identified and handled missing data to improve the quality of the dataset. Statistical Analysis: Performed correlation analysis to identify relationships between movie attributes. Data Visualization: Created various visualizations to clearly communicate the findings, including genre distribution, rating trends, and runtime analysis. Results Identified significant correlations between certain genres and audience ratings, with a correlation coefficient of 0.6 for some genres. Improved data accuracy by cleaning and preprocessing the dataset, reducing inconsistencies by 30%. Generated over 20 visualizations to effectively convey the analysis results, enhancing the clarity and accessibility of the insights.
- Clone this repository to your local machine: git clone https://github.com/Audii98/Movie-Correlation-Project
- Navigate to the project directory: cd Movie-Correlation-Project
- Install the necessary Python packages: pip install -r requirements.txt
- Open the Jupyter Notebook to explore the analysis: jupyter notebook Movie_Correlation_Project.ipynb
If you'd like to contribute to this project, feel free to fork the repository and submit a pull request. Contributions are welcome!
This project is licensed under the MIT License. See the LICENSE file for details.
If you have any questions or feedback, please feel free to reach out via audumbar97@gmail.com.